Designing a Conversational Travel Recommender System Based on Data-Driven Destination Characterization

Recommending complex, intangible items in a domain with high consequences, such as destinations for traveling, requires additional care when deriving and confronting the users with recommendations. In order to address these challenges, we developed CityRec, a destination recommender that makes two contributions. The first is a data-driven approach to characterize cities according to the availability of venues and travel-related features, such as the climate and costs of travel. The second is a conversational recommender system with 180 destinations around the globe based on the data-driven characterization, which provides prospective travelers with inspiration for and information about their next trip. An online user study with 104 participants revealed that the proposed system has a significantly higher perceived accuracy compared to the baseline approach, however, at the cost of ease of use.

Full paper available as open access on CEUR-WS.